10 of 10 MLflow Jobs in the South East

track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems. Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML More ❯

distributed computing. Familiarity with CI/CD, version control, and DevOps practices. Nice-to-Have Experience with streaming technologies (e.g., Spark Structured Streaming, Event Hub, Kafka). Knowledge of MLflow, Unity Catalog, or advanced Databricks features. Exposure to Terraform or other IaC tools. Experience working in Agile/Scrum environments. To apply for this role please submit your CV or More ❯

experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). More ❯

have: Experience deploying machine learning models into production and managing their lifecycle. Experience implementing model governance, including versioning, monitoring, drift detection, and reporting. Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC. Programming skills in Scala. Experience working with ONNX and ONNX Runtime for model optimization and deployment. Experience mentoring or supporting colleagues to help them grow their technical More ❯

and managing scalable AI solutions on at least one major cloud platform (AWS, GCP, Azure). Solid understanding of MLOps principles and experience with relevant tools (e.g., Docker, Kubernetes, MLflow) for building robust, production-ready systems. The Ideal Candidate Profile: This role is a perfect fit if you... Are at the bleeding edge of AI: You don't just follow More ❯

component of our ML platform. Requirements You Are: Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost Skilled in developing classification models and anomaly detection systems for production environments Capable of implementing comprehensive data lineage More ❯

You will be designing, developing, and deploying cutting-edge machine learning solutions across the company.If you enjoy end-to-end ownership (from experimentation to deployment), working with AWS, Docker, MLflow, TensorFlow/PyTorch, and contributing to innovative projects with a flexible, results-focused culture, this could be an exciting match. Skills: Ability to write and produce production-grade code in More ❯

Machine Learning Engineer Machine Learning Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's … in. Our client is seeking Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI More ❯

heavily on governance, reproducibility, and automation. Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance. MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform. DevOps for ML: Build and automate robust CI/… Engineering team. Key Skills: Must Have: MLOps: Proven experience designing and implementing end-to-end MLOps processes in a production environment. Cloud ML Stack: Expert proficiency with Databricks and MLflow . Big Data/Coding: Expert Apache Spark and Python engineering experience on large datasets. Core Engineering: Strong experience with GIT for version control and building CI/CD/… model fundamentals for optimisation) Familiarity with low-latency data stores (e.g., CosmosDB ). If you have the capability to bring MLOps maturity to a traditional Engineering team using the MLFlow/Databricks/Spark stack, please email: with your CV and contract details. More ❯

scale. In this role, you will: Build robust ML and MLOps pipelines for scalable, reproducible model development, deployment, and monitoring. Use tools such as Airflow for workflow orchestration and MLflow for experiment tracking, model registry, and lifecycle management. Work within an agile development environment and help prioritise high-value opportunities for rapid delivery. Essential Skills Bachelors degree (2:1 or … STEM field Strong Python development skills Hands-on experience developing ML and/or deep learning models for scientific or engineering problems Experience with MLOps tools such as Airflow, MLflow, and containerisation (e.g., Docker) Strong data-visualisation and storytelling skills Interest in materials discovery, computer vision, big data, or optimisation Collaborative communicator, organised, proactive, and curious Desired Skills Masters degree More ❯